DESCRIPTION: (Applicant's Description) The overall goal of this proposal is to develop and test protein-based technologies for characterizing the protein profiles of complex tumor-related samples, particularly proteins and protein fragments secreted by human tumor tissues. The proposed studies will focus primarily on developing improved technologies in several critical areas. One working hypothesis is that effective analyses of human proteomes will require improved protein separation methods capable of resolving most of the proteins in the proteome, which are estimated to contain more than 10,000 protein components. A second working hypothesis is that existing in situ proteolysis methods are very inefficient when using femtomole protein amounts, which limits overall sensitivity of mass spectrometry (MS)-based protein identifications. A major emphasis will be to develop reproducible, robust 2D or 3D protein methods capable of separating >10,000 protein components. These efforts will include: improve conventional immobiline-based 2D gel, increase 2D gel resolution by using overlapping pH gradients, and develop 3D separation methods using pre-isofocusing ion exchange chromatography to produce several discrete reproducible groups of proteins. A second major emphasis will be to systematically evaluate and improve femtomole level proteolysis of gel spots for subsequent MS analyses, and to improve the confidence level of protein assignments using MS and MS-MS data. One objective will be to ensure extensive coverage of the entire protein sequence to help distinguish between closely related protein isoforms, alternatively spliced forms of proteins, and highly homologous protein modules shared by different proteins in complex human proteomes. This proposal involves four Specific Aims: 1) develop high resolution, higher capacity 2D or 3D proteome separation methods; 2) develop reliable microscale protease digestion of gel spots for MS and MS-MS analyses, 3) determine parameters that contribute to definitive protein identification using MS and MS-MS data; and 4) develop high throughput proteome identification methods by automating appropriate stages of the process.